Cross-Asset Correlation & Cointegration Intelligence [NikaQuant]
**Cross-Asset Correlation & Cointegration Intelligence**
Track your chart symbol against up to six comparison symbols. The script
renders **three synchronized panels** that tell you, in plain numbers:
- How coupled the basket is **right now**
- Which pairs are genuinely tradeable (and the **expected mean-reversion time**)
- How much **gross exposure** you should carry given the current regime
## What It Does
- **Intelligence Dashboard** — per-symbol grid: correlation, beta, R²,
z-score, percentile, stability, lead/lag, spread z, quality score,
hedge size, stress-vs-normal correlation delta, signal verdict
- **N×N Correlation Matrix** — full 6×6 pairwise heatmap
- **Action Center** — regime timer, flip probability, risk-budget advisor,
top-5 ranked trades, top-3 cointegrated pair setups, trade playbook
## Why It Is Original
Unlike standard correlation heatmap scripts that display a single Pearson
value per pair, this script builds a composite intelligence layer across
**three independent axes** that no retail correlation indicator combines:
**1. Asymmetric (Conditional) Correlation**
Splits history into **normal-volatility** and **stress-volatility** regimes
using an ATR-median split on the base symbol, and reports the two
correlations side by side. This exposes the *"diversification fails when
you need it"* amplification that an averaged Pearson value hides — a
documented pattern in every crisis since 1998.
**2. Cointegration + Half-Life**
For all 15 unique pairs, runs an **Engle-Granger two-step** (log-regression
then AR(1) on the residual spread) to flag which spreads are genuinely
mean-reverting. Cointegrated pairs carry an **Ornstein-Uhlenbeck half-life**
t½ = −ln(2) / ln(1 + φ) — the expected mean-reversion time in bars.
*Correlation tells you direction; cointegration tells you whether the
spread will revert.*
**3. Regime Persistence + Flip Probability**
Tracks four states (Crisis / Coupled / Mixed / Decoupled) in a **4×4 Markov
transition counter**, stores per-regime dwell times, and converts them into
flip-probability estimates for the next 10 and 30 bars. You see not just
*"we are in X"* but *"X has lasted 47 bars, historical average is 62 bars,
probability of flip in 30 bars is 55%."*
## Composite Modules
- **Crisis Clock (0–100)** — composite of average absolute correlation,
cross-sectional dispersion collapse, and tail-dependence count
- **Market Brain** — union-find clustering on positive pairwise
correlations, auto-groups symbols that move as one
- **Dispersion Trade Detector** — fires when average correlation drops
>2σ while realized volatility rises
- **Hedge Desk** — converts OLS beta into a **dollar hedge notional**
given your base position size
- **Effective-N** — correlation-adjusted diversification count (six
symbols at ρ=1.0 gives effective N = 1)
- **Risk Budget Advisor** — regime + effective-N → suggested gross
exposure percentage
- **Setup Quality Score** — composite of |corr| × R² × stability,
adjusted for regime, clock, and break
- **Action List** — scans every symbol and every pair, scores each
candidate, ranks them, surfaces the top five with type, target, score,
direction, suggested size, rationale
## Per-Symbol Metrics
- Rolling Pearson correlation across **three lookbacks** (short, medium,
long) — three-block glyph reveals timeframe divergence
- **OLS beta** from log returns, **R²** as variance explained
- **Z-score** of current correlation vs its own 200-bar distribution
- **Percentile rank** of current correlation in its own history
- **Stability** from rolling stdev of the correlation itself
- **Optimal-lag scanner** across {−5, −3, −1, 0, +1, +3, +5} offsets
- **Spread z-score** of the price ratio for pairs signaling
- **Asymmetric Δ** = ρ_stress − ρ_normal (positive = hedge fails under stress)
## How To Use It
- **Scan the Quality column first.** Anything at or above 60 with a
TRACK++ or HEDGE++ signal is a high-confidence setup.
- **Cross-check AsymΔ.** Values above +0.3 mean that "hedge" is expected
to fail under stress — avoid relying on it in a crisis.
- **Use Hedge column values** as the dollar notional to short or long
against your base position to neutralize beta.
- **Read the matrix** like a portfolio risk report. Clusters of dark-green
tiles = diversification is breaking down. Red tiles = inverse pairs.
- **In the Action Center**, start at the Risk Budget line, then work
top-down through the Action List. Cointegrated pairs marked with a
check-mark prefix show expected mean-reversion time in bars.
**Recommended timeframes:** intraday or daily charts with at least 250
bars of history across all six symbols.
**Recommended markets:** anywhere the base asset has meaningful
relationships with a benchmark basket — equity indexes vs sector ETFs,
crypto majors vs index proxies, FX vs rates and commodities.
**Avoid using when:** fewer than three symbols resolve to valid data;
during the first 250 bars after chart load; or on a symbol with gapped
or illiquid history that creates artificial correlation jumps.
## Alerts
Regime Break · Dispersion Trade Setup · New Cointegrated Pair ·
High-Quality Setup · Imminent Regime Flip · Crisis Regime Entered ·
Asymmetric Correlation Amplification · Risk Budget Reduced · Clock
Stressed/Critical · Strong Positive/Negative Correlation Crossovers
## Key Settings
- **Comparison Symbols 1–6** — the basket (autocomplete from any TV ticker)
- **Medium-Term Correlation Period** (50) — primary correlation lookback
- **Short / Long Lookbacks** (20 / 200) — timeframe-divergence glyph
- **Historical Baseline** (200) — z-score, percentile, stability, regime
dwell times, and asymmetric-correlation ATR split
- **Strong Correlation** (0.70) — threshold for strong-signal eligibility
- **Regime-Break |Z|** (2.00) — flags correlations breaking their range
- **Pairs-Trade |Spread Z|** (2.00) — pairs-trade setup threshold
- **Min R² for Trust** (0.25), **Min Stability** (0.50) — quality gates
- **Cluster Threshold** (0.60) — Market Brain grouping
- **Crisis Clock** — Stressed (60), Critical (80) thresholds
- **Base Position Size** (10,000) — drives Hedge Desk and Action sizing
- **Min Action Quality** (60) — filters the Action List
- **Risk Budget per regime** — Crisis 50%, Coupled 75%, Mixed 90%,
Decoupled 100% (all user-tunable)
- **Display** — position each of three tables independently; Compact or
Pro column density; full palette customization
## Notes
**No repainting.** All correlations, betas, and regime computations use
confirmed bars only. Regime transition counters and dwell-time arrays
update only when a bar confirms.
**Data integrity.** Six external symbol requests are made with
non-forward-looking data fetches, well within TradingView's request limit.
**Methods.** Asymmetric correlation uses log returns with indicator
weights from an ATR-median volatility split on the base asset.
Cointegration is Engle-Granger two-step: log-regression residual, then
AR(1) test. A pair is flagged as cointegrated when the AR(1) coefficient
is sufficiently negative to indicate mean-reversion. Half-life uses the
standard Ornstein-Uhlenbeck solution t½ = −ln(2) / ln(1 + φ).
**Warm-up.** The first ~250 bars after chart load are a warm-up period.
Several metrics will display "—" until enough history accumulates.
**Originality.** All calculations, signal logic, clustering,
cointegration testing, and table rendering are original. No third-party
code is reused.
อินดิเคเตอร์
Sector Divergence DashboardStatistical arbitrage dashboard for markets and sector ETFs
This Sector Divergence Dashboard is a tool designed to identify mean-reversion opportunities across U.S. equity sectors. I've built it to help me with portfolio management and sector allocation by identifying uncorrelated sectors and divergences between indices and sector ETFs. These divergences are often good investment opportunities.
The indicator also helps you with sector rotation by identifying when sectors have diverged too far from their historical relationships with the broader market. This is a similar methodology used daily in institutional portfolio management and hedge funds.
In this dashboard, you can see:
Z-Score Analysis on log price ratios to detect statistical anomalies
Dual-timeframe correlation tracking to identify relationship breakdowns
Composite scoring that combines divergence magnitude, correlation shifts, and momentum
Correlation heatmap for instant relationship assessment across all pairs
You see exactly which pairs are statistically mispriced and likely to revert to their historical mean.
NOTE: This dashboard is computationally heavy and might take up to one minute to load in your TradingView.
The Mathematics
1. Price Ratio Z-Score
The indicator calculates the logarithmic price ratio between two assets (e.g., SPY/XLE) and measures how many standard deviations this ratio has moved from its historical average. A z-score of +2.0 means the pair is 2 standard deviations expensive relative to history. This can be a mean-reversion setup.
2. Correlation Breakdown Detection
Short-term correlation (35 bars) is compared against long-term correlation (100 bars). You can change these parameters BTW. When correlations diverge significantly, it signals that the normal relationship has temporarily broken, potentially creating trading opportunities.
3. Relative Performance
Measures the momentum difference between pairs over 300 bars (roughly 60 weeks on daily charts). This captures longer-term structural shifts versus short-term noise.
4. Composite Score
All three metrics are normalized and weighted to create a single ranking score:
50% Z-Score Weight - Primary driver of mean reversion probability
25% Correlation Breakdown - Relationship stability metric
25% Relative Performance - Momentum/trend context
Features
1. Correlation Heatmap
Visualize all pairwise correlations
Color-coded from red (negative correlation) to green (strong positive)
Spot which sectors are moving together or decoupling
2. Divergence Rankings Table
Top 15 SPY-vs-sector pairs ranked by composite opportunity score
Z-scores, correlations, performance differentials, and signals
Color-coded from gray (neutral) to red (extreme divergence)
Scan it daily for setups
3. Deep Dive Chart
Detailed z-score visualization for any selected pair
Visual zones showing normal range, signal threshold, and strong signal areas
Short-term and long-term correlation overlays
Real-time information label with current metrics and signal status
Perfect for analyzing specific opportunities in depth
You can define which symbols you want to deep dive in the parameters
Parameter Guide
Short-Term Correlation - Recent relationship strength
Long-Term Correlation - Historical baseline relationship
Z-Score Length - Mean reversion lookback period
Relative Performance - Longer-term momentum context
Pro Tip : Increase z-score length to 150+ for fewer but stronger signals. Decrease to 50-75 for more frequent opportunities (but more noisy).
Use Cases
Sector Rotation: Identify which sectors are over/undervalued relative to the market
Portfolio Rebalancing: Data-driven signals for tactical asset allocation adjustments
Pairs Trading: Statistical arbitrage between correlated instruments
Risk Management: Monitor correlation stability across your portfolio
Market Regime Detection: Spot when sector relationships are breaking down
Swing Trading: Mean-reversion setups with clear entry/exit rules
Example:
XLK, a key tech ETF, is typically very correlated with the S&P 500 with a 0.88 correlation. Our dashboard detected a divergence between both, which signals a buy/rotation to XLK.
Let me know if you have any requests, improvements suggestions or feedback :) อินดิเคเตอร์
Pair Creation🙏🏻 The one and only pair construction tech you need, unlike others:
Applies one consistent operation to all the data features (not only prices). Then, the script outputs these, so you can apply other calculations on these outputs.
calculates a very fast and native volatility based hedge ratio, that also takes into account point value (think SPY vs ES) so you can easily use it in position sizing
Has built-in forward pricing aka cost of carry model , so you can de-drift pairs from cost of carry, discover spot price of oil based on futures, and ofc find arbitrage opportunities
Also allows to make a pair as a product of 2 series, useful for triangular arbitrage
This script can make a pair in 2 ways:
Ratio, by dividing leg 1 by leg 2
Product, by multiplying leg 1 by leg 2
The real mathematically right way to construct a pair is a ratio/product (Spreads are in fact = 2 legged portfolio, but I ain't told ya that ok). Why? Because a pair of 2 entities has a mathematically unique beauty, it allows direct comparisons and relationship analysis, smth you can't do directly with 3 and more components.
Multiplication (think inversions like (EURUSD -> USDEUR), and use cases for triangular arbitrage) is useful sometimes too.
...
Quickguide:
First, "Legs" are pair components: make a pair of related assets. Don’t be guided exclusively by clustering, cointegrations, mutual information etc. Common sense and exogenous info can easily made them all Forward pricing model: is useful when u work with spot vs futures pairs. Otherwise: put financing, storage and yield all on zeros, this way u will turn it off and have a pure ratio/product of 2 legs.
Look at the 2 numbers on the script’s status line: the first one would always be 1), and the second one is a variable.
First number (always 1) is multiplier for your position size on leg 1
The second number is the multiplier for your position size on leg 2 in the opposite direction.
If both legs are related, trading your sizes with these multipliers makes you do statistical arbitrage -> trading ~ volatility in risk free mode, while the relationship between the assets is still in place.
Also guys srsly, nobody ‘ever’ made a universal law that somewhy somehow for whatever secret conspiracy reason one shall only trade pairs in mean reverting style xd. You can do whatever you want:
Tilt hedge ratio significantly based on relative strength of legs
Trade the pair in momentum style
Ignore hedge ratio all together
And more and more, the limit is your imagination, e.g.:
Anticipate hedge ratio changes based on exogenous info and act accordingly
Scalp a pair just like any other asset
Make a pair out of 2 pairs
Like I mean it, whatever you desire
About forward pricing model:
It’s applied only to leg 2;
Direct: takes spot price and finds out implied futures price
Inverse: takes futures price and finds out implied spot price (try on oil)
Pls read online how to choose parameters, it’s open access reliable info
About the hedge ratio I use:
You prolly noticed the way I prefer to use inferred volumes vs the “real” ones. In pairs it’s especially meaningful, because real volumes lose sense in pair creation. And while volumes are closely tied to volatility, the inferred volumes ‘Are’ volatility irl (and later can be converted to currency space by using point value, allowing direct comparisons symbol vs symbol).
This hedge ratio is a good example of how discovering the real nature of entities beats making 100s of inventions, why domain knowledge and proper feature engineering beats difficult bulky models, neural networks etc. How simple data understanding & operations on it is all you need.
This script simply does this:
Takes inferred volume delta of both assets, makes a ratio, normalizes it by tick sizes and points values of both legs, calculates a typical value of this series.
That’s it, no step 2, we’re done. No Kalman filters, no TLS regression, no vine copulas, or whatever new fancy keywords you can come up with etc.
...
^^ comparing real ES prices vs theoretical ones by forward-pricing model. Financing: 0.04, yield 0.0175
^^ EURUSD, 6E futures with theoretical futures price calculated with interest rate differential 0.02 (4% USD - 2% EUR interest rates)
^^4 different pairs (RTY/ES, YM/ES, NQ/ES, ES/ZN) each with different plot style (pick one you like in script's Style settings)
^^ YM/RTY pair, each plot represents ratio of different features: ratio of prices, ratio of inferred volume deltas, ratio of inferred volumes, ratio of inferred tick counts (also can be turned on/off in Style settings)
...
How can u upgrade it and make a step forward yourself:
On tradingview missing values are automatically fixed by backfilling, and this never becomes a thing until you hit high frequency data. You can do better and use Kalman filter for filling missing values.
Script contains the functions I use everywhere to calculate inferred volume delta, inferred volume, and inferred tick count.
...
∞
อินดิเคเตอร์
Static Beta for Pair and Quant Trading A beta coefficient shows the volatility of an individual stock compared to the systematic risk of the entire market. Beta represents the slope of the line through a regression of data points. In finance, each point represents an individual stock's returns against the market.
Beta effectively describes the activity of a security's returns as it responds to swings in the market. It is used in the capital asset pricing model (CAPM), which describes the relationship between systematic risk and expected return for assets. CAPM is used to price risky securities and to estimate the expected returns of assets, considering the risk of those assets and the cost of capital.
Calculating Beta
A security's beta is calculated by dividing the product of the covariance of the security's returns and the market's returns by the variance of the market's returns over a specified period. The calculation helps investors understand whether a stock moves in the same direction as the rest of the market. It also provides insights into how volatile—or how risky—a stock is relative to the rest of the market.
For beta to provide useful insight, the market used as a benchmark should be related to the stock. For example, a bond ETF's beta with the S&P 500 as the benchmark would not be helpful to an investor because bonds and stocks are too dissimilar.
Beta Values
Beta equal to 1: A stock with a beta of 1.0 means its price activity correlates with the market. Adding a stock to a portfolio with a beta of 1.0 doesn’t add any risk to the portfolio, but it doesn’t increase the likelihood that the portfolio will provide an excess return.
Beta less than 1: A beta value less than 1.0 means the security is less volatile than the market. Including this stock in a portfolio makes it less risky than the same portfolio without the stock. Utility stocks often have low betas because they move more slowly than market averages.
Beta greater than 1: A beta greater than 1.0 indicates that the security's price is theoretically more volatile than the market. If a stock's beta is 1.2, it is assumed to be 20% more volatile than the market. Technology stocks tend to have higher betas than the market benchmark. Adding the stock to a portfolio will increase the portfolio’s risk, but may also increase its return.
Negative beta: A beta of -1.0 means that the stock is inversely correlated to the market benchmark on a 1:1 basis. Put options and inverse ETFs are designed to have negative betas. There are also a few industry groups, like gold miners, where a negative beta is common.
LET'S START
Now I'll give my own definition.
Beta:
If we assume market caps are equal ,
it is an indicator that shows how much of the second instrument we should buy if we buy one of the first, taking into account the price volatility of two instruments.
But if the market caps are not equal:
For example, the ETF for A is $300.
The ETF for B is $600.
If static beta predicted by this script is 0.5:
300 * 1 * a = 600 * 0.5 * b
Then we should use 1 b for 1 a.
(Long a and short b or vice versa )
So, we can try pair trading for a/b or a-b.
However, these values are generally close to each other, such as 0.8 and 0.93. However, the closer we can adjust our lot purchases to bring the double beta to a value closer to 1, the higher the hedge ratio will be.
Large commercials use dynamic betas, which are updated periodically, in addition to static betas
However, scaling this is very difficult for individual investors with limited investment tools.
But a static beta of 5,000 bars is still much better than not considering any beta at all.
Note: The presence of a beta value for two instruments does not necessarily mean they can be included in pair trading.
It is also important (%99) to consider historically very high correlations and cointegration relationships, as well as the compatibility of security structures.
Note 2 : This script is designed for low timeframes.
Do not use betas from different timeframes.
Beta dynamics are different for each timeframe.
Note 3 : I created this script with the help of ChatGPT.
Source for beta definition ( ) :
www.investopedia.com
Regards. อินดิเคเตอร์
SMT Oscillator: Smarter Money Divergence Detector [PhenLabs]📊Phenlabs - SMT Oscillator: Smarter Money Divergence Detector
Version: PineScript™v6
📌Description
The SMT Oscillator is a sophisticated tool designed to identify smart money divergence between two correlated assets. By analyzing the momentum and volume-weighted price action of a primary and secondary symbol, traders can spot subtle shifts in market dynamics that often precede significant price movements. This indicator is built to provide a clearer, more filtered view of inter-market relationships, solving the common problem of false signals and market noise. Its primary purpose is to equip traders with a quantifiable edge in detecting potential reversals or continuations that are not obvious on a standard price chart.
🚀Points of Innovation
Dual-Symbol Divergence Core: Directly compares momentum (RSI or MACD) between two user-selected symbols to pinpoint true SMT divergence.
Volume-Weighted Analysis: Integrates volume delta into the divergence calculation, giving more weight to moves backed by significant market participation.
Entropy Filter for Noise Reduction: Employs an entropy calculation to filter out low-quality signals during choppy or consolidating market conditions.
Predictive Forecast Line: Utilizes a linear regression model to project the oscillator’s future trajectory, offering a forward-looking glimpse of potential momentum shifts.
Customizable Signal Sensitivity: Allows fine-tuning of overbought and oversold levels to adapt to different market volatilities and trading styles.
Integrated Signal Alerts: Provides built-in alerts for bullish/bearish zero crosses and overbought/oversold conditions.
🔧Core Components
Momentum Engine: The user can select either RSI or MACD as the underlying engine for the divergence calculation, allowing for flexibility in analysis.
Normalization Function: Price data from both symbols is normalized using percentage change to ensure a true “apples-to-apples” comparison, regardless of their nominal price differences.
Divergence Calculator: The core algorithm that subtracts the secondary symbol’s momentum from the primary’s and normalizes the result using the combined standard deviation.
Smoothing Mechanism: An Exponential Moving Average (EMA) is applied to the raw oscillator output to reduce choppiness and provide a clearer signal line.
🔥Key Features
Multi-Asset Comparison: Go beyond single-asset analysis by comparing correlated pairs like ES/NQ or BTC/ETH to uncover hidden trading opportunities.
Heatmap Visualization: An optional heatmap mode provides an intuitive visual representation of divergence strength, making it easier to gauge market sentiment at a glance.
Configurable Lookback and Timeframe: Adjust the lookback period and analysis timeframe to suit your specific strategy, from short-term scalping to long-term trend analysis.
Signal Markers: Visual markers are plotted directly on the chart for bullish and bearish zero-line crossovers, providing clear entry and exit signals.
🎨Visualization
SMT Oscillator Line: The primary visual element, colored blue for bullish (positive) divergence and orange for bearish (negative) divergence.
Zero Line: A solid horizontal line at the zero level, indicating the equilibrium point between the two assets. Crossovers of this line signal a shift in relative strength.
Overbought/Oversold Zones: Dotted lines at the +80 and -80 levels (customizable) that highlight extreme divergence readings, often indicating potential exhaustion points.
Forecast Line: A predictive line that plots the anticipated path of the oscillator, giving traders an advanced warning of potential changes in momentum.
📖Usage Guidelines
Setting Categories
Primary Symbol
Default: (Chart Symbol)
Description: The main asset you are analyzing. Leave blank to use the symbol currently on your chart.
Secondary Symbol
Default: CME_MINI:ES1! (used with NASDAQ futures due to inherent heavy correlation
Description: The asset to compare against the primary symbol.
Lookback Period
Default: 14
Range: 8-100
Description: Controls the calculation window for momentum (RSI/MACD). Higher values result in a smoother, less sensitive oscillator.
Divergence Type
Default: RSI
Options: RSI, MACD
Description: Choose the momentum indicator to use for the divergence calculation.
Enable Volume Weighting
Default: true
Description: When enabled, gives more weight to divergence signals that are accompanied by significant volume.
✅Best Use Cases
Identifying high-probability reversal points by spotting divergence in overbought or oversold territory.
Confirming the strength of a trend by observing sustained positive or negative divergence.
Pairs trading by taking a long position on the outperforming asset and a short position on the underperforming one during a divergence.
Risk management by recognizing when a current trend is losing its underlying momentum.
⚠️Limitations
Requires Correlated Assets: The indicator’s effectiveness is highly dependent on the selection of two assets with a known correlation (e.g., ES and NQ).
Not a Standalone System: Divergence signals should be used in conjunction with other forms of analysis (price action, market structure) and not as a complete trading system.
Lagging by Nature: As it is based on moving averages and past price data, the oscillator is inherently lagging and may not capture all rapid price changes.
💡What Makes This Unique
Combined Momentum & Volume: Unlike standard oscillators, it fuses momentum with volume delta for a more robust “Smart Money” perspective.
Noise-Filtering Mechanism: The proprietary entropy filter is a unique feature designed to weed out insignificant market chatter and focus on high-conviction signals.
🔬How It Works
Data Normalization:
The script first normalizes the price data of the two selected symbols into percentage changes. This ensures that the comparison is fair, regardless of the difference in their price scales.
Momentum Calculation:
It then calculates the chosen momentum value (either RSI or MACD histogram) for each of the normalized price series.
Divergence Computation:
The core of the indicator lies in subtracting the momentum of the secondary symbol from the primary one. This raw divergence is then optionally weighted by volume and filtered for market noise (entropy) to produce the final oscillator value.
💡Note:
For best results, use this indicator on adequate timeframes to filter out market noise. Always confirm signals with price action analysis before entering a trade. อินดิเคเตอร์
อินดิเคเตอร์
อินดิเคเตอร์
กลยุทธ์
Symbols Correlation, built for pair tradingOverview:
This script is designed for pairs trading. If you are not familiar with pairs trading, I suggest learning about it, as it can be a profitable strategy in neutral markets (or neutral trends between two assets). The correlation between two assets is the foundation of pairs trading, and without it, the chances of making a profit are low.
Correlation can be described in two opposite ways:
1: Absolute positive correlation (meaning the asset prices move together).
-1: Absolute negative correlation (meaning the asset prices move in opposite directions).
Any value between 1 and -1 indicates some degree of correlation, but generally, values higher than 0.7 or lower than -0.7 are considered significant.
Features:
Typically, correlation is measured using the closing prices. This script adds three more correlation studies based on open, high, and low prices. By using all four lines, we can get a better understanding of the pair's correlation.
How to Read This Indicator:
To use this indicator effectively, you need to input your pair as a ratio. For example, if your pair is TSN and ZBH, enter it in the symbol search as: TSN/ZBH
Gray Area : This area indicates "no high correlation" (default is between -0.8 and 0.8, adjustable in the settings).
Gray Line : This represents the close correlation within the "no high correlation" range.
Green Line : This represents the close correlation within the "high correlation" range.
Dot Lines : These represent the open, high, and low correlations.
Example Interpretations:
A : All four lines are close together & the line is green – very good correlation!
B : The line is gray, and the dot lines are apart – not a strong correlation.
C : When the close correlation remains green for a long time, it signals a strong correlation.
Application in Pairs Trading:
In pairs trading, aim for the highest possible correlation, and it is important to have a sustained correlation over a long period. Pairs that correlate only part of the year but not consistently are less reliable for pairs trading.
This is an example for good correlation for pairs trading:
This is an example for bad correlation for pairs trading:
Here is a view of my full indicators when doing pairs trading:
อินดิเคเตอร์
Trend Momentum Strength Indicator, Built for Pairs TradingOverview:
This script combines multiple indicators to provide a comprehensive analysis of both trend strength and trend momentum. It is tailored specifically for pairs trading strategies but can also be used for other trading strategies.
Benefit of Comprehensive Analysis:
Having an indicator that evaluates both trend strength and trend momentum is crucial for traders looking to make informed decisions. It allows traders to not only identify the direction and intensity of a trend but also gauge the momentum behind it. This dual capability helps in confirming potential trade opportunities, whether for entering trades with strong trends or considering reversals during overbought or oversold conditions. By integrating both aspects into one tool, traders can gain a holistic view of market dynamics, enhancing their ability to time entries and manage risk effectively.
Features:
* Trend Strength:
Enhanced ADX Formula: The script includes modifications to the standard ADX formula along with DI+ and DI- to provide more responsive trend strength readings.
Directional Indicators: DI+ (green line) indicates positive directional movement, while DI- (red line) indicates negative directional movement.
Trend Momentum:
Modified Stochastic Indicators: The script uses %K and %D indicators, modified and combined with ADX to give a clear indication of trend momentum.
Momentum Strength: This helps determine the strength and direction of the momentum.
Trading Signals:
Combining Indicators: The script combines ADX, DI+, DI-, %K, and %D to generate comprehensive trading signals.
Optimal Entry Points: Designed to identify optimal entry points for trades, particularly in pairs trading.
Colored Area at Bottom:
This area provides two easy-to-read functions:
Color:
Green: Upward momentum (ratio above 1)
Red: Downward momentum (ratio below 1)
Height:
Higher in green: Stronger upward momentum
Lower in red: Stronger downward momentum
Legend:
Green Line: DI+ (Positive)
Red Line: DI- (Negative)
Black Line: ADX
How to Read This Indicator:
1) Trend Direction:
DI+ above DI-: Indicates an upward trend.
DI- above DI+: Indicates a downward trend.
2) Trend Strength:
ADX below 20: Indicates a neutral trend.
ADX between 20 and 25: Indicates a weak trend.
ADX above 25: Indicates a strong trend.
Trading Signals in Pairs Trading:
Neutral Trend: Ideal for pairs trading when no strong trend is detected.
Overbought/Oversold: Uses %K and %D to identify overbought/oversold conditions that support trade decisions.
Entry Signals: Green signals for long positions, red signals for short positions, based on combined criteria of neutral trend strength and supportive momentum.
Application in Pairs Trading:
Neutral trend: In pairs trading strategies, where neutral movement is often sought, this indicator provides signals that are especially relevant during periods of neutral trend strength and supportive momentum, aiding traders in identifying optimal entry
Risk Management: Combining signals from ADX, DI+, DI-, %K, and %D helps traders make more informed decisions regarding entry points, enhancing risk management.
Example Chart (The indicator is on the upper right corner):
Clean Presentation: The chart only includes the necessary elements to demonstrate the indicator’s functionality.
Demonstrates: Overbought/oversold conditions, upward/downward/no momentum, and trading signals with/without specific scenarios.
อินดิเคเตอร์
อินดิเคเตอร์
กลยุทธ์
อินดิเคเตอร์
กลยุทธ์
กลยุทธ์
